Selection of filter parameters based on signal quality

ABSTRACT

Methods and devices for reducing noise effects in a system for measuring a physiological parameter, including receiving an input signal, obtaining an assessment of the signal quality of the input signal, selecting coefficients for a digital filter using the assessment of signal quality; and filtering the input signal using the digital filter, without comparing the filter&#39;s output signal with the input signal.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.11/247,427, filed Oct. 11, 2005, which is a Continuation of U.S. Pat.No. 7,016,715, filed Jan. 13, 2003, the disclosures of which are herebyincorporated by reference for all purposes.

BACKGROUND

Present embodiments relate to the processing of signals obtained from amedical diagnostic apparatus, such as a pulse oximeter, using a digitalfilter to reduce noise effects.

A typical pulse oximeter measures two physiological parameters, percentoxygen saturation of arterial blood hemoglobin (SpO₂ or sat) and pulserate. Oxygen saturation can be estimated using various techniques. Inone common technique, the photocurrent generated by the photo-detectoris conditioned and processed to determine the ratio of modulationrations (ratio of ratios) of the red to infrared signals. Thismodulation ratio has been observed to correlate well to arterial oxygensaturation. The pulse oximeters and sensors are empirically calibratedby measuring the modulation ratio over a range of in vivo measuredarterial oxygen saturations (SaO₂) on a set of patients, healthyvolunteers, or animals. The observed correlation is used in an inversemanner to estimate blood oxygen saturation (SpO₂) based on the measuredvalue of modulation ratios of a patient. The estimation of oxygensaturation using modulation ratios is described in U.S. Pat. No.5,853,364, entitled “METHOD AND APPARATUS FOR ESTIMATING PHYSIOLOGICALPARAMETERS USING MODEL-BASED ADAPTIVE FILTERING,” issued Dec. 29, 1998,and U.S. Pat. No. 4,911,167, entitled “METHOD AND APPARATUS FOR DETECINGOPTICAL PULSES,” issued Mar. 27, 1990. The relationship between oxygensaturation and modulation ratio is further described in U.S. Pat. No.5,645,059, entitled “MEDICAL SENSOR WITH MODULATED ENCODING SCHEME,”issued Jul. 8, 1997. Most pulse oximeters extract the plethysmographicsignal having first determined saturation or pulse rate, both of whichare susceptible to interference.

A challenge in pulse oximetry is in analyzing the data to obtain areliable measure of a physiologic parameter in the presence of largeinterference sources. Various solutions to this challenge have includedmethods that assess the quality of the measured parameter and decide ondisplaying the measured value when it is deemed reliable based upon asignal quality. Another approach involves a heuristic-based signalextraction technology, where the obtained signals are processed based ona series of guesses of the ratio, and which require the algorithm tostart with a guess of the ratio, which is an unknown. Both thesignal-quality determining and the heuristic signal extractiontechnologies are attempts at separating out a reliable signal from anunreliable one, one method being a phenomenological one and the otherbeing a heuristic one.

A known approach for the reduction of noise in medical diagnosticdevices including pulse oximeters involves the use of an adaptivefilter, such as an adaptive digital filter. The adaptive filter isactually a data processing algorithm, and in most typical applications,the filter is a computer program that is executed by a centralprocessor. As such, the filter inherently incorporates discrete-timemeasurement samples rather than continuous time inputs. A type ofdigital filter that is used in pulse oximeter systems is a Kalmanfilter. While conventional adaptive digital filters in general andKalman filters in particular have been assimilated in medicaldiagnostics system to help reduce noise in a signal, there are stillmany challenges that need to be addressed to improve the techniques thatare used to reduce noise effects in signals; noise effects such as thosepresent in a medical diagnostic device. One of the shortcomings of usinga Kalman filter is that a Kalman filter is an adaptive filter whosefunctioning is mathematically-based and where its aim is to compare theoutput of the filter with a desired output, and reduce the error in thecomparison by continuously varying the filter's coefficients. So, aKalman filter generates filter coefficients in an adaptive manner tominimize an error. While this method has been adopted by many, it isstill a method that is somewhat blind regarding the signal that it isbeing filtered. Such an approach does not take into account the uniqueattributes that an input signal may possess and which arephysiologically based. Another shortcoming of the Kalman filtering isthat the Kalman filter is linear in its input-output relationship. Onecan appreciate that in certain conditions, the requirement that thefilter be linear in its input-output relationship is too constrainingYet another shortcoming of a Kalman filter is that filter parameters arecontinuously tuned, which can be computationally expensive.

There is therefore a need to develop a filter for reducing noise effectsin signals that does not suffer from the above-mentioned constraints ofconventional adaptive filters.

BRIEF SUMMARY

Present embodiments are directed towards methods and devices forreducing noise effects in a system for measuring a physiologicalparameter, including receiving an input signal; obtaining an assessmentof the signal quality of the input signal; selecting coefficients for adigital filter using the assessment of a signal quality; and filteringthe input signal using the digital filter, without comparing thefilter's output signal with the input signal.

In certain aspects, the filter coefficients are selected from aplurality of discrete preset values. In certain embodiments, thediscrete and preset values are fixed or non-changing values. The digitalfilter can have either a linear or preferably a non-linear input-outputrelationship.

In pulse oximetry applications, the quality of the input signal may beassessed by obtaining or measuring signal parameters that include theskew of the time derivative of the signal; the correlation betweensignals from different wavelengths; the variation in signal amplitude,as well as others. Other assessments, such as maximum values or spectralpeak frequencies, may also be used in determining filter parameters.

In some embodiments, the selection of filter parameters or coefficientsis performed in real time, with the coefficients of the digital filterbeing determined using a current input sample. In certain otherembodiments, the selection of filter parameters is performed using apreviously stored input signal sample.

In pulse oximetry applications, the input signals can be a function ofan oxygen saturation, or a pulse rate. Furthermore, these signalscorrespond with sensed optical energies from a plurality of wavelengths.

For a further understanding of a nature and advantages of the presentembodiments, reference should be made to the following description takenin conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary oximeter.

FIG. 2 is a block diagram depicting the operation of thesignal-quality-based filter operation in accordance with embodiments.

DETAILED DESCRIPTION

The methods and systems in accordance with embodiments are directedtowards selecting and adjusting the parameters of a digital filter basedan assessment of the quality of the input signals to the filter.Embodiments are particularly applicable to and will be explained byreference to measurements of oxygen saturation of hemoglobin in arterialblood and patient heart rate, as in pulse oximeter monitors and pulseoximetry sensors. However, it should be realized that the presentembodiments are equally applicable to any generalized patient monitorand associated patient sensor, such as ECG, blood pressure, temperature,etc., and is not to be limited for use only with oximetry or pulseomimetry.

FIG. 1 is a block diagram of one embodiment of a pulse oximeter that maybe configured to implement embodiments. The filter embodiments can be adata processing algorithm that is executed by the microprocessor 122,described below. Light from light source 110 passes into patient tissue112, and is scattered and detected by photodetector 114. A sensor 100containing the light source and photodetector may also contain anencoder 116 which provides signals indicative of the wavelength of lightsource 110 to a detector/decoder 144 in a pulse oximeter 120 to allowthe oximeter to select appropriate calibration coefficients forcalculating oxygen saturation. Encoder 116 may, for instance, be aresistor.

Sensor 100 is connected to a pulse oximeter 120. The oximeter includes amicroprocessor 122 connected to an internal bus 124. Also connected tothe bus is a RAM memory 126 and a display 128. A time processing unit(TPU) 130 provides timing control signals to light drive circuitry 132which controls when light source 110 is illuminated, and if multiplelight sources are used, the multiplexed timing for the different lightsources. TPU 130 also controls the gating-in of signals fromphotodetector 114 through an amplifier 133 and a switching circuit 134.These signals are sampled at the proper time, depending upon which ofmultiple light sources is illuminated, if multiple light sources areused. The received signal is passed through an amplifier 136, a low passfilter 138, and an analog-to-digital converter 140. The digital data isthen stored in a queued serial module (QSM) 142, for later downloadingto RAM 126 as QSM 142 fills up. In one embodiment, there may be multipleparallel paths of separate amplifier filter and A/D converters formultiple light wavelengths or spectrums received.

Based on the value of the received signals corresponding to the lightreceived by photodetector 114, microprocessor 122 will calculate theoxygen saturation using various algorithms. These algorithms requirecoefficients, which may be empirically determined, corresponding to, forexample, the wavelengths of light used. These are stored in a ROM 146.In a two-wavelength system, the particular set of coefficients chosenfor any pair of wavelength spectrums is determined by the valueindicated by encoder 116 corresponding to a particular light source in aparticular sensor 100. In one embodiment, multiple resistor values maybe assigned to select different sets of coefficients. In anotherembodiment, the same resistors are used to select from among thecoefficients appropriate for an infrared source paired with either anear red source or far red source. The selection between whether thenear red or far red set will be chosen can be selected with a controlinput from control inputs 154. Control inputs 154 may be, for instance,a switch on the pulse oximeter, a keyboard, or a port providinginstructions from a remote host computer. Furthermore, any number ofmethods or algorithms may be used to determine a patient's pulse rate,oxygen saturation or any other desired physiological parameter.

The brief description of an exemplary pulse oximeter set forth above,serves as a contextual fabric for describing the methods for reducingnoise effects in the received signals according to embodiments, whichare described below. The embodiments which are used to reduce the noiseeffects in the signal using an assessment of the quality of the inputsignal, are described below in conjunction with the block diagram ofFIG. 2.

A signal quality indicator is a measured parameter that is capable ofestimating the reliability and accuracy of a signal. For example, whenmeasuring blood oxygen saturation using a pulse oximeter, a signalquality indicator is able to indirectly assess whether an estimate of avalue of blood oxygen saturation is an accurate one. This determinationof accuracy is made possible by the thorough and detailed study ofvolumes of measured values and various indicators to determine whichindicators are indicative of signal quality and what, if any, is thecorrelation between the indicator and the accuracy of the estimatedvalue.

In pulse oximetry, examples of signal quality indicators include theskew of the time derivative of the signal; the correlation betweensignals from different wavelengths; the variation in signal amplitude,as well as others. Other assessments, such as maximum values or spectralpeak frequencies, may also be used in determining filter parameters. Inaddition to these signal quality indicators, other signal qualityindicators may also be used for the selection of filter coefficients. Inpulse oximetry, these additional signal quality indicators include: asignal measure indicative of the degree of similarity of an infrared andred waveforms; a signal measure indicative of a low light level; asignal measure indicative of an arterial pulse shape; a signal measureindicative of the high frequency signal component in the measure value;a signal measure indicative of a consistency of a pulse shape; a signalmeasure indicative of an arterial pulse amplitude; a signal measureindicative of modulation ratios of red to infrared modulations and asignal measure indicative of a period of an arterial pulse. Thesevarious indicators provide for an indirect assessments of the presenceof known error sources in pulse oximetry measurements, which includeoptical interference between the sensor and the tissue location; lightmodulation by other than the patient's pulsatile tissue bed; physicalmovement of the patient and improper tissue-to-sensor positioning. Theseadditional signal quality indicators are described in further detail ina co-pending US patent application entitled: “SIGNAL QUALITY METRICSDESIGN FOR QUALIFYING DATA FOR A PHYSIOLOGICAL MONITOR,” U.S. Pat. No.7,006,856, filed Jan. 10, 2003, the disclosure of which is hereinincorporated by reference in its entirety for all purposes.

FIG. 2 is a block diagram 200 depicting the operation of thesignal-quality-based selection of filter parameters in accordance withembodiments. In one embodiment, the digital filter is a linear filter.For a linear filter is chosen, the filter can have either a finite or aninfinite impulse response. Alternately, the filter may be a non-linearfilter. Inputs 202 are applied to the digital filter 204 and to a signalquality assessment subsystem 206 that assesses how noisy the inputslook. Subsystem 206 calculates various signal quality metrics andprovides the information to the selection subsystem 208, which selectsfilter parameters according to the criteria calculated by the signalquality subsystem 206. Storage subsystem 210 interfaces with thesubsystems 206 and 208 to store and provide signal quality metrics aswell as filter parameters. In one embodiment, the selection of filterparameters is performed in real time, with the filter parameters beingdetermined using current input samples.

In an alternate embodiment, the filter parameters are calculated using abuffer 212 of recent input samples. In addition, signal assessmentcriteria and filter parameters can also be held in storage 210 forreference or for use in the calculation of new values.

As set forth above, various signal quality indicators may be used toselect filter parameters. Additionally, the selection of the filterparameters may be based on more than one signal quality indicator.Furthermore, the selection of the filter parameters may be based on theoutput of an algorithm that combines several signal quality indicators.In one embodiment in an oximeter system, the variance in the rawsaturation value is used to determine the filter's smoothingcoefficients. In this embodiment, the selection is achieved by comparingthe variance in the raw sat signal to several thresholds, and thefilter's smoothing coefficients are selected depending on which rangethe variance falls in.

In an alternate embodiment in an oximeter system used for average pulseestimation, the filter parameter selection algorithm uses a combinationof various signal quality metrics, Z to select values for filtercoefficients for the digital filter, where

Z=w₁*SQ1+w₂*SW2+w₃*SQ3, where

w₁, w₂, and w₃ are weighting factors

SQ1 is a measure of the variance in the raw saturation signal

SQ2 is a measure of the correlation between signals from differentwavelengths

SQ3 is a measure of the skew of the derivative waveform

Yet alternately, instead of using Z to select the filter coefficients, anon-linear function of Z can be used to select a coefficient orcoefficients for the filter. In operation, the selection algorithm mayfirst be tuned before it is fully implemented in a particulardiagnostics system. The tuning of the selection algorithm(s) may be donemanually using heuristic approaches. Alternately, the selectionalgorithm may be tuned statistically, in a manner similar to training aneural network.

Embodiments offer several advantages over conventional adaptivefiltering. It is known that conventional adaptive filtering seeks tooptimize some output criterion by continuously tuning the coefficientsin a linear filter. The approach is advantageous over conventionaladaptive filtering for the following reasons. First, filter parametersin accordance with embodiments are selected by switching among severalpreset or fixed values, rather than being varied or tuned continuously.By switching the parameters of the digital filter among fixed, presetvalues, the embodiments provide for computational savings and simplicityof implementation. Second, the parameters of the digital filter areselected based upon an assessment of the input signal received by thefilter rather than by comparing the filter's output with its input. Thistoo, provides for computational savings and simplicity ofimplementation. Third, the filter need not be a linear filter, that isthe filter is not required to be linear in its input-outputrelationship. Since the filter in accordance with embodiments is notconstrained to be linear, the filter's design can correspond more tophysiological than to mathematical requirements, as is the case withmost conventional adaptive filtering schemes. This physiological-basedfilter parameter selection may be used to, for example, attenuate pulseamplitudes above a threshold, or respond more quickly to decreases thanto increases in blood oxygen saturation.

Accordingly, as will be understood by those of skill in the art,embodiments related to reducing noise effects in a system for measuringa physiological parameter, may be embodied in other specific formswithout departing from the essential characteristics thereof Forexample, signals indicative of any physiological parameter other thanoxygen saturation, such as pulse rate, blood pressure, temperature, orany other physiological variable could be filtered using the techniquesabove. Moreover, many other indicators of the quality of the inputsignal can be used as a basis for the selection of the filter'scoefficients. Further, while the present embodiments have been describedin the time-domain, frequency-base methods are equally relevant to theembodiments. Accordingly, the foregoing disclosure is intended to beillustrative, but not limiting.

1. A method, comprising: receiving, at a processor, an input signal;determining, with the processor, an assessment of signal quality for theinput signal by determining a skew of a time derivative of the inputsignal or a variation in signal amplitude of the input signal;selecting, with the processor, one or more coefficients for a digitalfilter using the assessment of signal quality without comparing anoutput of the digital filter with the input signal; and filtering theinput signal using the digital filter.
 2. The method of claim 1, whereindetermining, with the processor, the assessment of signal qualitycomprises determining the skew of a time derivative of the input signaland determining the variation in signal amplitude of the input signal.3. The method of claim 2, wherein determining, with the processor, theassessment of signal quality comprises applying a first weight to theskew of the time derivative of the input signal and applying a secondweight to the variation in signal amplitude of the input signal.
 4. Themethod of claim 1, wherein selecting, with the processor, the one ormore coefficients for the digital filter comprises selecting one or morecoefficients from a plurality of discrete preset values.
 5. The methodof claim 1, wherein the digital filter comprises a linear digitalfilter.
 6. The method of claim 1, wherein the digital filter comprises anon-linear digital filter.
 7. The method of claim 1, wherein receiving,at the processor, the input signal comprises receiving the input signalfrom a pulse oximetry sensor, and wherein the input signal comprises ared waveform and an infrared waveform.
 8. The method of claim 7, whereindetermining, with the processor, the assessment of signal quality forthe input signal comprises determining a measure indicative of a degreeof similarity between the red waveform and the infrared waveform.
 9. Themethod of claim 7, wherein determining, with the processor, anassessment of signal quality for the input signal comprises determininga measure indicative of a low light level.
 10. The method of claim 7,wherein determining, with the processor, an assessment of signal qualityfor the input signal comprises determining a measure indicative of anarterial pulse shape.
 11. A monitor, comprising: a processor configuredto: receive an input signal from a medical sensor; determine anassessment of signal quality for the input signal by determining a skewof a time derivative of the input signal or a variation in signalamplitude of the input signal; and determine one or more digital filtercoefficients using the assessment of signal quality; and a digitalfilter configured to filter the input signal using the one or moredigital filter coefficients, wherein the processor is configured todetermine the one or more digital filter coefficients without comparingan output of the digital filter to the input signal.
 12. The monitor ofclaim 11, wherein the digital filter comprises a linear digital filter.13. The monitor of claim 11, wherein the processor is configured todetermine the skew of a time derivative of the input signal and thevariation in signal amplitude of the input signal to determine theassessment of signal quality for the input signal.
 14. The monitor ofclaim 13, wherein the processor is configured to apply a first weight tothe skew of the time derivative of the input signal and apply a secondweight to the variation in signal amplitude of the input signal todetermine the assessment of signal quality for the input signal.
 15. Themonitor of claim 11, wherein the medical sensor comprises a pulseoximetry sensor and the input signal comprises a red waveform and aninfrared waveform.
 16. The monitor of claim 15, wherein the processor isconfigure to determine a measure indicative of a degree of similaritybetween the red waveform and the infrared waveform to determine theassessment of signal quality of the input signal.
 17. A system,comprising: a medical sensor configured to generate a physiologicalsignal; a processor configured to: receive the physiological signal fromthe medical sensor; determine an assessment of signal quality for thephysiological signal by determining a skew of a time derivative of thephysiological signal or a variation in signal amplitude of thephysiological signal; and determine one or more digital filtercoefficients using the assessment of signal quality; and a digitalfilter configured to filter the physiological signal using the one ormore digital filter coefficients; wherein the processor is configured todetermine the one or more digital filter coefficients without comparingan output of the digital filter to the physiological signal.
 18. Thesystem of claim 17, wherein the medical sensor comprises a pulseoximetry sensor, and wherein the processor is configured to determine ameasure indicative of an arterial pulse shape to determine theassessment of signal quality for the physiological signal.
 19. Thesystem of claim 18, comprising a memory storing a plurality of digitalfilter coefficients, and wherein the processor is configured to selectthe one or more digital filter coefficients from the plurality ofdigital filter coefficients.
 20. The system of claim 17, wherein theprocessor is configured to determine the skew of the time derivative ofthe physiological signal and the variation in signal amplitude of thephysiological signal and to apply a first weight to the skew of the timederivative of the physiological signal and a second weight to thevariation in signal amplitude of the physiological signal to determinethe assessment of signal quality of the physiological signal.